The aim of this GIS project is to investigate the differences in using R and QGIS as the chosen GIS tool. The data used in this project is timeseries data on median income for tax payers in United Kingdom, from 1999 to 2014. The available data covers individual boroughs in London as well as regional and national areas in United Kingdom. The conclusion of this project is that each program has its advantage, and so in respect to the question about which one is the better, it depends on the goal of the task. R does a better job on the Data Science related tasks, but QGIS makes it easier to handle the visualization part of the job.

This short project is structured such that it should be clear to see dis-/advantages of each program. Below follows a short description of the data, both the raw and processed data that at the end is visualised. The project rounds of with an in-depth description of the dis-/advantages of both programs.

This GIS project intents to visualise the differences in growth multiples of median earnings between boroughs in London. While it causes divergence peoples living standards, it is also a drag on each individual borough’s propensity to invest in innovation. According to some economists, innovation is the main driver of future growth for economies on advanced growth paths. Furthermore, innovation can take economies from one growth path to another, and therefore should investing in innovation be a priority for all societies.

The first thing we do is to read in the raw data that contains the median earnings for each borough in London, as well as regional and national regions in United Kingdom.

## # A tibble: 5 x 6
##   X__1     X__2            `1999-00`         X__3  X__4   `2000-01`       
##   <chr>    <chr>           <chr>             <chr> <chr>  <chr>           
## 1 Code     Area            Number_of_Indivi~ Mean~ Media~ Number_of_Indiv~
## 2 E090000~ City of London  10000             1098~ 40400  10000           
## 3 E090000~ Barking and Da~ 62000             16200 15100  71000           
## 4 E090000~ Barnet          161000            26800 18700  156000          
## 5 E090000~ Bexley          105000            20500 17200  116000

Above can be seen a snippet of the raw data. This is the data from where the average annualized growth multiple is being calculated. It should be obvious from the above snippet, that the data isn’t as clean as one could wish, and more important, it is not at all ready to be loaded in to QGIS.

A growth multiple for each year, for each borough, is calculated and can be seen from the snippet below.

##        Code                 Area     2000      2001      2002     2003
## 2 E09000001       City of London 1.608911 0.5892308 0.9660574 1.059459
## 3 E09000002 Barking and Dagenham 1.033113 1.0384615 1.0493827 1.023529
## 4 E09000003               Barnet 1.037433 0.9639175 1.0534759 1.015228
## 5 E09000004               Bexley 1.005814 1.0578035 0.9726776 0.994382
## 6 E09000005                Brent 1.018182 0.9821429 0.9939394 1.024390

In this project, we are only interested in the average annualized growth multiple, and it is the one that we plot in the map. The average growth multiple for each borough is visualized on the map below.

Below is the coming description of the dis-/advantages of each program.